# Copyright 2019 BDL Benchmarks Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Script for training and evaluating Mean-Field Variational Inference baseline
for Diabetic Retinopathy Diagnosis benchmark."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import os

import tensorflow as tf
from absl import app
from absl import flags
from absl import logging

import bdlb
from bdlb.core import plotting
from model import VGGFlipout
from model import predict

tfk = tf.keras

##########################
# Command line arguments #
##########################
FLAGS = flags.FLAGS
flags.DEFINE_string(
    name="output_dir",
    default="/tmp",
    help="Path to store model, tensorboard and report outputs.",
)
flags.DEFINE_enum(
    name="level",
    default="medium",
    enum_values=["realworld", "medium"],
    help="Downstream task level, one of {'medium', 'realworld'}.",
)
flags.DEFINE_integer(
    name="batch_size",
    default=128,
    help="Batch size used for training.",
)
flags.DEFINE_integer(
    name="num_epochs",
    default=50,
    help="Number of epochs of training over the whole training set.",
)
flags.DEFINE_integer(
    name="num_mc_samples",
    default=10,
    help="Number of Monte Carlo samples used for uncertainty estimation.",
)
flags.DEFINE_enum(
    name="uncertainty",
    default="entropy",
    enum_values=["stddev", "entropy"],
    help="Uncertainty type, one of those defined "
    "with `estimator` function.",
)
flags.DEFINE_integer(
    name="num_base_filters",
    default=32,
    help="Number of base filters in convolutional layers.",
)
flags.DEFINE_float(
    name="learning_rate",
    default=4e-4,
    help="ADAM optimizer learning rate.",
)
flags.DEFINE_float(
    name="dropout_rate",
    default=0.1,
    help="The rate of dropout, between [0.0, 1.0).",
)
flags.DEFINE_float(
    name="l2_reg",
    default=5e-5,
    help="The L2-regularization coefficient.",
)


def main(argv):

  print(argv)
  print(FLAGS)

  ##########################
  # Hyperparmeters & Model #
  ##########################
  input_shape = dict(medium=(256, 256, 3), realworld=(512, 512, 3))[FLAGS.level]

  hparams = dict(num_base_filters=FLAGS.num_base_filters,
                 learning_rate=FLAGS.learning_rate,
                 input_shape=input_shape)
  classifier = VGGFlipout(**hparams)
  classifier.summary()

  #############
  # Load Task #
  #############
  dtask = bdlb.load(
      benchmark="diabetic_retinopathy_diagnosis",
      level=FLAGS.level,
      batch_size=FLAGS.batch_size,
      download_and_prepare=False,  # do not download data from this script
  )
  ds_train, ds_validation, ds_test = dtask.datasets

  #################
  # Training Loop #
  #################
  history = classifier.fit(
      ds_train,
      epochs=FLAGS.num_epochs,
      validation_data=ds_validation,
      class_weight=dtask.class_weight(),
      callbacks=[
          tfk.callbacks.TensorBoard(
              log_dir=os.path.join(FLAGS.output_dir, "tensorboard"),
              update_freq="epoch",
              write_graph=True,
              histogram_freq=1,
          ),
          tfk.callbacks.ModelCheckpoint(
              filepath=os.path.join(
                  FLAGS.output_dir,
                  "checkpoints",
                  "weights-{epoch}.ckpt",
              ),
              verbose=1,
              save_weights_only=True,
          )
      ],
  )
  plotting.tfk_history(history,
                       output_dir=os.path.join(FLAGS.output_dir, "history"))

  ##############
  # Evaluation #
  ##############
  dtask.evaluate(functools.partial(predict,
                                   model=classifier,
                                   num_samples=FLAGS.num_mc_samples,
                                   type=FLAGS.uncertainty),
                 dataset=ds_test,
                 output_dir=FLAGS.output_dir)


if __name__ == "__main__":
  app.run(main)
